System and method for delivery of content based on matching of user profiles with content metadata

ABSTRACT

In accordance with an embodiment, described herein is a system and method for delivery of content based on matching of user profiles with content metadata. The system enables delivery of personalized content, without the overhead of managing segment targeting rules, while providing content publishers or marketers with complete control over such personalization. A recommendation service or application program interface, provided by a computer, cloud computing environment, or other type of computer system, enables receipt and processing of requests, from client devices, for personalized content. A recommendation engine delivers content assets in response to a request from a client device. The recommendation engine determines a content channel and a user identity associated with the request, and then delivers content assets based on rules governing the matching of content asset metadata with the user profile. While content classification evolves over time, so also does the personalization of delivered content.

CLAIM OF PRIORITY

This application is a continuation of U.S. patent application titled“SYSTEM AND METHOD FOR DELIVERY OF CONTENT BASED ON MATCHING OF USERPROFILES WITH CONTENT METADATA”, application Ser. No. 16/785,370, filedFeb. 7, 2020; which claims the benefit of priority to U.S. ProvisionalPatent Application titled “SYSTEM AND METHOD FOR DELIVERY OF CONTENTBASED ON MATCHING OF USER PROFILES WITH CONTENT METADATA”, ApplicationNo. 62/803,201, filed Feb. 8, 2019; each of which above applications andthe contents thereof are herein incorporated by reference.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever.

TECHNICAL FIELD

Embodiments described herein are generally related to systems andmethods for delivery of content in association with online, web, mobile,or other types of computer environments, and are particularly related todelivery of personalized content based on a matching of user profileswith content metadata.

BACKGROUND

Some content targeting systems, as may be implemented in associationwith online, web, mobile, or other e-commerce or computer environments,operate by allowing a definition of target segments of users, and thentargeting particular data or content to those segments.

For example, in one approach, a number of target segments can be createdusing conditional segment targeting rules, that are then applied to eachuser's profile attributes.

However, such an approach does not scale well with an increasing numberof target segments. In particular, as the number of target segmentsincreases, with users potentially belonging to multiple differentsegments, it becomes increasingly difficult for, say, a business user todetermine which particular content to associate with which particularsegment. This shortcoming is particularly evident in environments wherethe target content is continually being updated, where the user profilescontinually evolve, or where the target segments are continually beingredefined.

An alternative approach to content targeting is to employ contenttargeting models that are largely driven by computer machine-learning.With this approach, content publishers or marketers generally defercontrol of determining the specific content to be presented to each userpersona, and instead allow a computer itself to make such decisions.

However, the quality of the recommendations that are provided using amachine-learning approach is largely reliant on the amount of trial datawhich was previously gathered and used to train the machine-learningmodels. The approach also suffers from an inherent delay in the time ittakes for the system to learn from, and respond to, aggregate userbehavior.

SUMMARY

In accordance with an embodiment, described herein is a system andmethod for delivery of content based on matching of user profiles withcontent metadata. The system enables delivery of personalized content,without the overhead of managing segment targeting rules, whileproviding content publishers or marketers with complete control oversuch personalization. A recommendation service or application programinterface, provided by a computer, cloud computing environment, or othertype of computer system, enables receipt and processing of requests,from client devices, for personalized content. A recommendation enginedelivers content assets in response to a request from a client device.The recommendation engine determines a content channel and a useridentity associated with the request, and then delivers content assetsbased on rules governing the matching of content asset metadata with theuser profile. While content classification evolves over time, so alsodoes the personalization of delivered content.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for delivery of content, in accordance withan embodiment.

FIG. 2 further illustrates a system for delivery of content, inaccordance with an embodiment.

FIG. 3 further illustrates a system for delivery of content, inaccordance with an embodiment.

FIG. 4 further illustrates a system for delivery of content, inaccordance with an embodiment.

FIG. 5 further illustrates a system for delivery of content, inaccordance with an embodiment.

FIG. 6 further illustrates a system for delivery of content, inaccordance with an embodiment.

FIG. 7 illustrates a method for delivery of content, in accordance withan embodiment.

FIG. 8 illustrates an example of recommendation rules as a continuum, inaccordance with an embodiment.

FIG. 9 further illustrates an example of recommendation rules as acontinuum, in accordance with an embodiment.

FIG. 10 illustrates the use of a graphical user interface to configureor interact with a system for delivery of content, in accordance with anembodiment.

FIG. 11 illustrates an example of a graphical user interface, and usagethereof, for use with a system for delivery of content, in accordancewith an embodiment.

FIG. 12 illustrates another example of a graphical user interface, andusage thereof, for use with a system for delivery of content, inaccordance with an embodiment.

FIG. 13 illustrates another example of a graphical user interface, andusage thereof, for use with a system for delivery of content, inaccordance with an embodiment.

FIG. 14 illustrates another example of a graphical user interface, andusage thereof, for use with a system for delivery of content, inaccordance with an embodiment.

FIG. 15 illustrates another example of a graphical user interface, andusage thereof, for use with a system for delivery of content, inaccordance with an embodiment.

FIG. 16 illustrates another example of a graphical user interface, andusage thereof, for use with a system for delivery of content, inaccordance with an embodiment.

FIG. 17 illustrates another example of a graphical user interface, andusage thereof, for use with a system for delivery of content, inaccordance with an embodiment.

FIG. 18 illustrates another example of a graphical user interface, andusage thereof, for use with a system for delivery of content, inaccordance with an embodiment.

FIG. 19 illustrates another example of a graphical user interface, andusage thereof, for use with a system for delivery of content, inaccordance with an embodiment.

FIG. 20 illustrates another example of a graphical user interface, andusage thereof, for use with a system for delivery of content, inaccordance with an embodiment.

FIG. 21 illustrates another example of a graphical user interface, andusage thereof, for use with a system for delivery of content, inaccordance with an embodiment.

FIG. 22 illustrates another example of a graphical user interface, andusage thereof, for use with a system for delivery of content, inaccordance with an embodiment.

FIG. 23 illustrates another example of a graphical user interface, andusage thereof, for use with a system for delivery of content, inaccordance with an embodiment.

DETAILED DESCRIPTION

As described above, content targeting systems, as may be implemented inassociation with online, web, mobile, or other e-commerce or computerenvironments, operate in accordance with various approaches, forexample, either by allowing a definition of target segments of users,and then targeting particular content to those segments usingconditional segment targeting rules; or alternatively through the use ofcontent targeting models that are largely driven by computermachine-learning; with each such approach having various associatedadvantages and disadvantages.

An additional consideration is that some content targeting systems maynot have access to metadata describing the content, and instead mustresort to associating the actual content with various target segments ofusers, each time a new piece of content is added to the system.

Furthermore, some content targeting systems operate primarily on theclient-side, for example in a web browser environment, by replacing abrowser's domain object model with targeted content. Such client-basedapproaches are generally less capable of supporting newer channels ofcontent delivery, such as, for example, through the use of mobileapplications, verbally-driven devices, or online conversational agentsor chatbots.

In accordance with an embodiment, described herein is a system andmethod for delivery of content based on matching of user profiles withcontent metadata. The system enables delivery of personalized content,without the overhead of managing segment targeting rules, whileproviding content publishers or marketers with complete control oversuch personalization.

In accordance with an embodiment, a recommendation service orapplication program interface, provided by a computer, cloud computingenvironment, or other type of computer system, enables receipt andprocessing of requests, from client devices, for personalized content. Arecommendation engine delivers content assets in response to a requestfrom a client device. The recommendation engine determines a contentchannel and a user identity associated with the request, and thendelivers content assets based on rules governing the matching of contentasset metadata with the user profile. While content classificationevolves over time, so also does the personalization of deliveredcontent.

In accordance with an embodiment, a technical purpose or advantage ofthe systems and methods described herein is that a computer system soprogrammed can automatically determine delivery of an appropriatecontent (e.g., a best match) to end users, based, for example, on theuser identity, profile data, the channels used for accessing content,and a past behavior; without needing to first create numerous usersegments.

For example, in accordance with an embodiment, content publishers ormarketers, who are interested in targeting appropriate content to users,can use the system to employ a configuration-driven targeting to achievea best matching of content, even for a small sample set of users andcontent, including in some instances smaller sets with which amachine-learning model might otherwise fail to recommend appropriatecontent.

In accordance with an embodiment, the described systems and methods canbe used to best match content for a particular user, based on the user'sprofile (user profile) and a content metadata associated with variouscontent; while taking into account features such as contentclassification, tagging, or a semantic meaning of that particularcontent.

As described in accordance with various embodiments, additional featurescan include, for example:

(1) Content Matching: In accordance with an embodiment, the system caninclude a recommendation engine that automatically matches user profiles(e.g., Age=18, City=“New York”, Occupation=) against attributes assignedto content, instead of, for example, a content publisher or marketerbeing required to explicitly configure the content for target segments.

(2) Best Matching: In accordance with an embodiment, even if theavailable content does not exactly match all user profile attributes,the system can select and rank-order a best matching content (forexample, content that matches a user's city and gender, but not theirage) by priority, with the understanding that in the absence of an exactmatch, the best matching content is still better than non-personalizedcontent, and is likely sufficient for most scenarios. By comparison,conventional content targeting systems generally attempt to deliverexact matches based on a combination of AND/OR conditions.

(3) Taxonomy: In accordance with an embodiment, the system can take intoconsideration a content classification taxonomy structure, and providematch recommendations based on a nearest content. For example, inaccordance with an embodiment that matches content to visitors fromvarious geographic locations, if there is a visitor from “Los Angeles”,and there is no matching content for “Los Angeles”, then the system canmatch proximal content classified, for example, as “California” or “SanDiego”, which is still better than non-personalized content.

(4) Artificial Intelligence (AI) and Natural Language Processing (NLP)based matching: In accordance with an embodiment, the system can use AIand NLP techniques to match content that is semantically near a user'sprofile. For example, in accordance with an embodiment, dog fanciers maybe provided with a selection of images of puppies, even though aparticular image may not be classified as a “dog”.

In accordance with an embodiment, the approach to delivery ofpersonalized content based on matching of user profiles with contentmetadata, as described herein, can take advantage of the system'sknowledge of content, such as for example, its knowledge of topics,classification, tags, relationships, or semantic meaning associated withthe content; over any manual determination of target segments (whichrequires much user input), or use of machine-learning models (which takea long time and results in loss of business user control).

In accordance with an embodiment, the described approach benefits fromhaving direct access to a content management system; a knowledge of thesystem's fine-grained content classification and metadata; and tightcoupling with the system's content store; which aspects have generallynot been provided by conventional content targeting systems.

Additionally, in accordance with an embodiment, the approach to deliveryof personalized content based on matching of user profiles with contentmetadata, as described herein, is server-based, such that it can beeasily adapted to accommodate multiple different client types, and newerchannels of content delivery.

In accordance with an embodiment, when compared with conventionalapproaches, that generally operate at different ends of a spectrum, forexample, at one end a largely manual environment requiring thedefinition of potentially thousands of target segments; while at theother end a completely computer AI-controlled system—the describedapproach provides a middle ground that avoids the need to collect largeamounts of training data, while still providing personalization andcontrol over content delivery, across multiple channels.

System Architecture

FIG. 1 illustrates a system for delivery of personalized content, inaccordance with an embodiment.

As illustrated in FIG. 1, in accordance with an embodiment, a contentmanagement system 100 can be provided at a computer environment, cloudcomputing environment, or other type of computer system that includesphysical computer resources 102 (e.g., processor/CPU, memory), and isoperable to provide access to a database or repository of stored contentassets, referred to herein in some embodiments as an asset repository140; and manage a content metadata 110 that is associated with thecontent assets.

In accordance with an embodiment, an example of an asset repository, andcontent assets, can be those provided by, for example, an Oracle Contentand Experience (OCE) environment, or another type of asset repository ordatabase; and an example of a content metadata can be that provided byOCE content metadata, or another type of content metadata.

The above examples of asset repository or database, content assets, orcontent metadata are provided in accordance with an embodiment, and forpurposes of illustration. In accordance with various embodiments, thesystem can operate with other types of asset repository or database,content assets, or content metadata.

Depending on the particular embodiment, the asset repository can includeor store the actual content metadata describing the content assets thatare stored therein. Alternatively, the content metadata can be storedelsewhere within the system.

Additionally, depending on the particular embodiment, the content itselfcan be stored within (e.g., owned by) the content management system; or,alternatively, the content can be stored outside of the contentmanagement system, for example as a proxy content, and accessed, forexample, by pointers to that content.

In accordance with an embodiment, the system can further include arecommendation builder 104, and a recommendation engine 106, each ofwhich can be provided as software or program code executable by acomputer system or other processing device. The recommendation builderand recommendation engine operate according to a plurality ofrecommendation rules that together configure the system to compare userprofile attributes with the content metadata associated with the contentassets; and, for example, determine a ranked collection of one or morecontent assets to return, in response to a request for personalizedcontent, that, for example, exactly match a user profile, or best matchthe user profile.

In accordance with an embodiment, for each user of a plurality of users,a user identity is determined in association with one or more sessionson one or more content channels, for example using an identity mapperand event tracker 120.

In accordance with an embodiment, an example of an identity mapper andevent tracker can be that provided by, for example, an Oracle CX Unityenvironment, an Oracle Infinity environment, or another type of identitymapper or event tracking system.

For example, in accordance with an embodiment, a CX Unity environment isadapted to bring together various online, offline, and third-partycustomer data sources to create a single, dynamic view of a customer;while an Infinity environment provides a digital analytics platform fortracking, measuring, and optimizing the performance and visitor behaviorof enterprise websites and mobile applications, including an analyticsapplication that provides a set of report collections for exploring andmanaging data.

The above examples of identity mappers and event trackers are providedin accordance with an embodiment, and for purposes of illustration. Inaccordance with various embodiments, the system can operate with othertypes of identity mappers and event trackers.

In accordance with an embodiment, as further described below, a datadescribing user profiles, and user profile attributes, associated withthe plurality of users, can also be received from one or more additionalprofile sources.

In accordance with an embodiment, a recommendation service orapplication program interface (API) 108 enables receipt and processingby the recommendation engine of requests, from client devices 130, forpersonalized content, whereupon the recommendation engine specifiesdelivery of content assets, including, in response to a particularrequest from a client device, determining a content channel and aparticular user identity associated with the particular request, andthen determining content assets based on rules governing the matching ofcontent asset metadata with the user's profile.

In accordance with an embodiment, each different type of client device,such as, for example, web (e.g., browser) 132, mobile application(s) 34,and custom applications(s) 136, can be associated with different APIs ornative software development kits (SDKs) 138 that are appropriate tothose types of devices.

Generally described, in accordance with an embodiment, therecommendation service or API exercises the logic of the recommendationengine, and can be accessed by clients at various endpoints, to issuerequests for content; while the recommendation builder builds therecommendations, for use by the recommendation engine, which are thenexposed by the recommendation service or API.

The above examples of different types of client devices are provided inaccordance with an embodiment, and for purposes of illustration. Inaccordance with various embodiments, the system can operate with othertypes of clients or client devices.

FIG. 2 further illustrates a system for delivery of personalizedcontent, in accordance with an embodiment.

As described above, in accordance with an embodiment, a data describinguser profiles and user profile attributes associated with the pluralityof users can be received from one or more additional profile sources.

In accordance with an embodiment, such profile information can generallybe received by a variety of methods, including: long term user profileinformation, and real-time tracking information.

In accordance with an embodiment, long term user profile information canbe received via a connector, for example, from a CX Unity environment,or another type of user profile aggregator.

In accordance with an embodiment, real-time tracking information, forexample information describing a user's browsing history, can bereceived as event information, which provides micro-event tracking, suchas, for example the time spent by a user on a particular web page,clicks, or hovering.

As illustrated in FIG. 2, in accordance with an embodiment, additionaluser profile information can be received from a user/customer profiledatabase 124, for example via a profile connector or service provideinterface (SPI) 125; and/or via a user profile aggregator 122 whichprovides a user profile based on identity 123.

In accordance with an embodiment, the above components can alsointeroperate with a marketing automation environment 128 (e.g., OracleEloqua) that enables access by a content publisher or marketer todevelop marketing processes across multiple channels, create customerprofiles or to strategically filter and segment audiences to creategranular segments based on attributes, for example behavior, orgeography.

In accordance with an embodiment, an example of a user profileaggregator can be provided by a CX Unity environment, or another type ofuser profile aggregator; while an example of a marketing automationenvironment can be provided by an Oracle Marketing Cloud (OMC)environment, an Eloqua environment, or by another type of marketingautomation environment.

The above examples of user profile aggregator and marketing automationenvironment are provided in accordance with an embodiment, and forpurposes of illustration. In accordance with various embodiments, thesystem can operate with other types of user profile aggregators andmarketing automation environments.

In accordance with an embodiment, the received information can bepackaged as attributes of the user profile; against which therecommendation engine can apply recommendation rules (for example, basedon taxonomy, tags, or semantic aspects) to determine a recommendation.The determined recommendation can then be used by a delivery service,client, or other mechanism, to fetch the associated content from theasset repository.

FIG. 3 further illustrates a system for delivery of personalizedcontent, in accordance with an embodiment.

As illustrated in FIG. 3, in accordance with an embodiment, a request150 for recommendation or personalized content is received at therecommendation service/API, and is associated with a set of user profileattributes 151.

In accordance with an embodiment, the request is passed 152 to therecommendation engine, which performs an attribute-metadata matching156, for use in determination and delivery 158 of a recommendation orpersonalized content.

In accordance with an embodiment, depending on the particular channel(s)160 used for, or associated with, the request, an appropriatepersonalized content 162, 164, 166, is delivered to the client devices.

FIG. 4 further illustrates a system for delivery of personalizedcontent, in accordance with an embodiment. For example, as illustratedin FIG. 4, in accordance with various embodiments, additionalfunctionality can be employed within the system, for example the use ofsmart tagging and/or automatic classification 170.

Example Implementation

In accordance with various embodiments, the system can employ some orall of the features described below, to address various use cases.Specific details are set forth in order to provide an understanding ofthe various embodiments. However, it will be apparent that variousembodiments may be practiced without these specific details. Theenclosed specification and drawings are not intended to be restrictive.

Best Match with Relevancy Ranking

In accordance with an embodiment, matches between user profileattributes and content metadata can be established by natural languageprocessing functions, including, for example, one or more of: a semanticmatch, or synonym match, of attribute to content metadata; a fuzzymatch, or stemmed match, of attribute to content metadata; and/or anamed entity match. The system can also be configured to return matcheswith a relevancy ranking.

Personalization System and Search Engine Optimization

In accordance with an embodiment, the system can support search engineoptimization (SEO) of, for example, a website or other onlineenvironment, by treating a search engine crawler as one or more distinctuser profiles, and serving distinct personalized content, which enablesthe search engine crawler to find distinct content, for use indetermining a search engine optimization score for the site.

Personalization Across Multiple Channels

In accordance with an embodiment, the system can be adapted to providepersonalization across multiple different channels, through the use ofan API architecture as described above, including the ability to track auser journey across these different channels, via a common user profiledatabase.

Graphical User Interface and Configuration

In accordance with an embodiment, the system can include a graphicaluser interface that provides a view of each user/customer's profileattributes that are mapped, for example, to OCE content metadata ortaxonomy, or another type of content metadata or taxonomy, forpersonalization.

In accordance with an embodiment, a profile attribute defines a visitorprofile attribute that is used for personalization. Some examples ofsources for these profile and other attributes can include:

(1) Profile attributes: For example, CX profile attributes retrievedfrom a CX Unity environment or other environments, for example,Responsys→Gender; Eloqua→First Name.

(2) Web Session attributes: For example, attributes extracted from anHTTP request, for example, IP address→geolocation, device type (e.g.,laptop, phone, tablet), URL parameters.

(3) Session History attributes: For example, attributes derived fromsession history (which requires collection and aggregation of sessiondata), for example, a new visitor versus a repeat visitor, or that aparticular customer “liked” a content item during their last visit.

(4) Complex Attribute: For example, an attribute that is not nativelyavailable in the profile views exposed to the recommendation engine, butconstructed from a data transform query, for example, on CX Unity data.Generally, this requires a query to create such an attribute, forexample: (1) most recent product purchased within the last three months;(2) repeat visitor on, for example, an OCE site within the last threemonths.

(5) Third-party Profile Attributes: For example, attributes collectedfrom third-party tools, such as BlueKai.

In accordance with an embodiment, identity mapping establishes theidentity of anonymous visitors on, for example, OCE sites or channels,who also visited other CX channels (for example, a Responsys landingpage, or Oracle Commerce Cloud) as registered users in the past. Thiscan be mapped via a common tag (e.g., an Infinity tag) that cansubsequently be used to retrieve their, e.g., CX profile, and deliverpersonalized content to such anonymous visitors in an OCE environment.

In accordance with an embodiment, the user profile aggregator aggregatesuser profile data from multiple sources, for example, an Infinityenvironment; and provides a common API to query user profile attributes.

In accordance with an embodiment, identity mapping and event trackingcan be used to tracks a user's anonymous identity across sessions;stitch identity across various touchpoints (e.g., email, web); and jointhe identity once the user logs in.

Example Delivery-Time Environment

In accordance with an embodiment, the received information from varioussources or environments can be packaged as facts; against which therecommendation engine can apply recommendation rules (for example, basedon taxonomy, tags, or semantic aspects) to determine a recommendation.The determined recommendation can then be used by a delivery service,client, or other mechanism to fetch the associated content from theasset repository.

FIG. 5 further illustrates a system for delivery of personalizedcontent, in accordance with an embodiment.

As illustrated in FIG. 5, in accordance with an embodiment, therecommendation engine can be programmed to operate a rules engine 202,including facts 204, and recommendation rules 206.

For example, in accordance with an embodiment, a request can beassociated with web attributes 210. The recommendation service or APIcan be associated with a connector 222, and short-lived cache 224, thatprovides access to an external data repository for user profile andevents 226.

In accordance with an embodiment, at delivery-time, in response toreceipt of a request 241, web attributes are determined 242, processedby the connector 242, and passed 244 to the recommendation engine; foruse in determining 245 a set of content assets that should berecommended, and passing 246 content asset identifiers to therecommendation service or API.

In accordance with an embodiment, the content asset identifiers are thenare used to fetch one or more content assets 247 from the assetrepository 140, which is/are then returned 248 to a client device.

Example Design-Time Environment

In accordance with an embodiment, the system can also be used to enablesetup or configuration of the recommendation engine, for example topreview what a particular user might see while interacting with thesystem. In accordance with an embodiment, the same delivery-timeenvironment as described above can be used, wherein the user is therecommendation builder itself.

FIG. 6 further illustrates a system for delivery of personalizedcontent, in accordance with an embodiment.

As illustrated in FIG. 6, in accordance with an embodiment, therecommendation builder can be used to create recommendation rules 251,and issue requests 252 to the recommendation service or API, for use indesigning rules and sites. The remainder of the process duringdesign-time is the same as during delivery-time, as described above. Theabove allows an administrator to, for example, build a new website andthen view the content on that site as a particular type of user.

For example, in accordance with an embodiment, a computer system 260that includes computer resources 262 (e.g., processor/CPU, memory), anda graphical user interface 264, can be provided to enable anadministrator 266 to review and modify content mapping configurations268, for use by the system, including the recommendation builder andrecommendation engine as described above.

Example Method for Delivery of Personalized Content

FIG. 7 illustrates a method for delivery of personalized content, inaccordance with an embodiment.

As illustrated in FIG. 7, in accordance with an embodiment, the methodcomprises, at step 272, providing, in a content management system,access to a repository of content assets, and metadata associated withthe content assets.

In accordance with an embodiment, the method further comprises, at step274, receiving, at a recommendation service or application programinterface, requests, from one or more client devices, for personalizedcontent.

In accordance with an embodiment, the method further comprises, at step276, determining, for each user of a plurality of users, a user identityassociated with one or more sessions or content channels.

In accordance with an embodiment, the method further comprises, at step278, receiving data describing user profiles and user profile attributesassociated with the plurality of users, from one or more profilesources.

In accordance with an embodiment, the method further comprises, at step280, comparing user profile attributes with content metadata, inaccordance with recommendation rules, to determine a ranked collectionof content assets to return in response to a request for personalizedcontent.

Recommendation Rules as a Continuum

Although the embodiments described above generally illustrate means forproviding a recommendation based, for example, on an evaluation of userprofile attributes and content attributes, and then determining a bestmatching content; an alternative approach is to consider recommendationrules provided within a continuum.

For example, a content publisher or marketer may not want to relinquishcontrol of determining specific content completely to machine-learning;but may instead be interested in some amount of machine enhancement to aparticular set of defined rules.

FIGS. 8 and 9 illustrate an example of recommendation rules as acontinuum, in accordance with an embodiment.

For example, as illustrated in FIG. 8, in accordance with an embodiment,a plurality of recommendation rules can be provided within arecommendation rule continuum 300 that varies, for example, from ahigher precision of targeted users 302, to a lower precision of targetedusers 304, including, in this example, recommendation rules A 310, B312, C 314, and D 316; wherein each rule is associated with a particulardegree of precision.

In accordance with an embodiment, those recommendation rules that areassociated with a higher degree of precision (e.g., rules A and B), forexample with regard to targeted users and/or content, allow greaterpublisher or marketer control, and rely less on machine control. Otherrecommendation rules that are associated with a lower degree ofprecision (e.g., rule C), rely more on machine control. Yet otherrecommendation rules that are associated with an even lower degree ofprecision (e.g., rule D), rely even more on machine control, and arewell suited to the use of machine control of best matching, as describedabove.

For example, in accordance with an embodiment, a content publisher ormarketer might configure a recommendation rule that operates so thatvisitors to a content site from “New York” (rule A) receive a New Yorkarea article; while visitors from “San Francisco” (rule B) receive a SanFrancisco area article; while visitors from “Chicago” or from “Houston”(rule C) receive instead a content that is based, say, on their industryvertical; while all other users (rule D), generally located within thetail of the continuum, receive instead a best matching content that isbased, say, on their personal interests,

In accordance with an embodiment, recommendation rules that areassociated with a highest precision, say of targeted users (e.g., rulesA, B) are considered exact target criteria, since both the user andcontent selection are well known. Rules associated with an intermediateprecision (e.g., rule C) may be equally precise with regard to users,but less precise with regard to content, and can utilize some aspects ofbest match. Rules associated with a lower precision (e.g., rule D) canbe handled as a best effort by the content targeting system.

In accordance with an embodiment, the higher the precision associatedwith a particular recommendation rule, the more the content publisher ormarketer can control the specific targeting associated with thatrule—such as for example, the targeting of specific content to certainusers; although such rules generally require more setup time.

In accordance with an embodiment, the system can also evaluate, over aperiod of time, information as to the best matches provided forparticular recommendation rules, and surface that information to thecontent publisher or marketer for use in creating newer (e.g.,higher-precision) rules, or providing other feedback for other use bythe system.

For example, as illustrated in FIG. 9, in accordance with an embodiment,a recommendation rule can be run and modified by the system for a periodof time, so that segments that are (originally) located within the tailof the continuum (e.g., Rules C, D) can be moved along the continuum,toward a more precise targeting of content for those users.

Additionally, in accordance with an embodiment, the use of a rulecontinuum enables the content publisher or marketer to perform, forexample, A/B testing, or other types of analysis, to determine whichrecommendations are likely to converge sooner within a period of time;or to determine new target segments or recommendation rules (e.g., rulesE 320, F 322); or tune or otherwise adjust recommendations that areappropriate for the various target segments.

Example Graphical User Interface

As described above, in accordance with an embodiment and as illustratedin FIG. 10, a computer system that includes computer resources (e.g.,processor/CPU, memory), and a graphical user interface, can be providedto enable an administrator to review and modify content mappingconfigurations, for use by the system, including the recommendationbuilder and recommendation engine as described above.

FIGS. 11-23 illustrate example graphical user interfaces, and usagethereof, for use in delivery of personalized content, in accordance withan embodiment.

As illustrated in FIGS. 11-23, in accordance with an embodiment, thegraphical user interface can be used to, for example, create arecommendation, test (preview) the recommendation by profile, defineoverrides for the recommendation, create a custom profile, or preview aparticular site by particular profile (e.g., impersonate a particularuser interacting with the site).

For example, using the above approach, the system can be used to treat acrawler on an Internet as a particular user/person, and provide data inan aggregate form to the crawler, for use in for use in determining asearch engine optimization score, or otherwise classifying the site.

In accordance with various embodiments, the teachings herein can beconveniently implemented using one or more conventional general purposeor specialized computer, computing device, machine, or microprocessor,including one or more processors, memory and/or computer readablestorage media programmed according to the teachings of the presentdisclosure. Appropriate software coding can readily be prepared byskilled programmers based on the teachings of the present disclosure, aswill be apparent to those skilled in the software art.

In some embodiments, the teachings herein can include a computer programproduct which is a non-transitory computer readable storage medium(media) having instructions stored thereon/in which can be used toprogram a computer to perform any of the processes of the presentteachings. Examples of such storage mediums can include, but are notlimited to, hard disk drives, hard disks, hard drives, fixed disks, orother electromechanical data storage devices, floppy disks, opticaldiscs, DVD, CD-ROMs, microdrive, and magneto-optical disks, ROMs, RAMs,EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic or opticalcards, nanosystems, or other types of storage media or devices suitablefor non-transitory storage of instructions and/or data.

The foregoing description has been provided for the purposes ofillustration and description. It is not intended to be exhaustive or tolimit the scope of protection to the precise forms disclosed. Manymodifications and variations will be apparent to the practitionerskilled in the art.

For example, although various embodiments as described above illustratehow the system can operate with, for example, an Oracle Content andExperience (OCE) environment, Oracle CX Unity environment, OracleInfinity environment, Oracle Marketing Cloud (OMC) environment, orEloqua environment, such examples are provided for purposes ofillustration; and in accordance with various embodiments, the system canoperate with other types of asset repository or database, contentassets, content metadata, identity mapper and event tracker, userprofile aggregator, or marketing automation environment

The embodiments were chosen and described in order to best explain theprinciples of the present teachings and their practical application,thereby enabling others skilled in the art to understand the variousembodiments and with various modifications that are suited to theparticular use contemplated. It is intended that the scope be defined bythe following claims and their equivalents.

What is claimed is:
 1. A system for delivery of personalized content,comprising: a computer including a processor, memory, and contentmanagement system operable to provide access to a repository of contentassets; wherein the system operates to process requests, from clientdevices, for delivery of personalized content, including determining acontent channel and a user identity associated with a request forpersonalized content, and determining, based on a content metadataassociated with the content assets, the content channel associated withthe request, and a dynamically-determined user profile, a particularcontent asset to deliver in response to the request, wherein thedynamically-determined user profile is based at least partly on userprofile information and real-time tracking of events received frommultiple sources; retrieving the particular content asset determined fordelivery from the repository of content assets; and delivering theparticular content asset to the client device during via the contentchannel associated with the request.
 2. The system of claim 1, whereinthe system includes a recommendation engine that operates according to aplurality of recommendation rules; wherein at delivery-time, in responseto receipt of the request for personalized content, a plurality ofattributes associated with the request are determined and passed to therecommendation engine, which: determines based on the request attributesand its recommendation rules, a set of best-matching content assets tobe recommended, and passes a corresponding set of content assetidentifiers to a recommendation service or application programinterface, for use in delivering the content assets.
 3. The system ofclaim 2, wherein the recommendation rules are provided as one or moretaxonomy, tags, or semantic rules, that provide a continuum of targetingfrom rules with a more precise targeting continuing through rulesassociated with a precise targeting, and wherein a determination of thecontent assets to be recommended that best match thedynamically-determined user profile or the request attributes, is basedat least on one or more of: a taxonomy of the content metadataassociated with the content assets; or a semantic matching of thecontent metadata associated with the content assets.
 4. The system ofclaim 1, wherein the dynamically-determined user profile is based atleast partly on data aggregated from a plurality of profile sources anddescribing a user profile and user profile attributes received from theprofile sources via a service provider interface and connectorassociated with each profile source.
 5. The system of claim 1, whereinfor each user of a plurality of users, the system tracks a user identityacross a plurality of sessions or content channels, which information isthen used in generating the dynamically-determined user profile.
 6. Thesystem of claim 1, further comprising a recommendation builder,including a plurality of interface screens that: receive as input,metadata associated with content assets; receive as input, definitionsof types of user profiles; receive as input, definitions ofrecommendation rules; receive as input, definitions of a web orapplication content managed in the content management system, includeone or more blog posts, articles, images, videos, banners, documents,frequently accessed questions, data records, lists, success stories thatassociates particular user profiles and user profile attributes withparticular recommendations; and enable processing of different types ofuser profiles, recommendation rules, and success stories, against thecontent metadata associated with content assets, for displaying previewsof the delivery of personalized content across different channels. 7.The system of claim 1, wherein, the system enables search engineoptimization of a website by treating a search engine crawler as one ormore distinct profiles and serving distinct personalized content, whichenables the search engine crawler to find distinct content, fordetermining a search engine optimization score for the site.
 8. Thesystem of claim 1, wherein comparison of user profile attributes withthe content metadata associated with the content assets, anddetermination of one or more content assets to return in response to arequest for personalized content is based on: a determination of contentassets that exactly match a user profile, or a determination of contentattributes that best match the user profile attributes; and determininga ranked collection of one or more content assets to return in responseto a request for personalized content.
 9. A method for delivery ofpersonalized content, comprising: providing, at a content managementsystem, access to a repository of content assets; and processingrequests, from client devices, for delivery of personalized content,including determining a content channel and a user identity associatedwith a request for personalized content, and determining, based on acontent metadata associated with the content assets, the content channelassociated with the request, and a dynamically-determined user profile,a particular content asset to deliver in response to the request,wherein the dynamically-determined user profile is based at least partlyon user profile information and real-time tracking of events receivedfrom multiple sources; retrieving the particular content assetdetermined for delivery from the repository of content assets; anddelivering the particular content asset to the client device during viathe content channel associated with the request.
 10. The method of claim9, wherein the system includes a recommendation engine that operatesaccording to a plurality of recommendation rules; wherein atdelivery-time, in response to receipt of the request for personalizedcontent, a plurality of attributes associated with the request aredetermined and passed to the recommendation engine, which: determinesbased on the request attributes and its recommendation rules, a set ofbest-matching content assets to be recommended, and passes acorresponding set of content asset identifiers to a recommendationservice or application program interface, for use in delivering thecontent assets.
 11. The method of claim 10, wherein the recommendationrules are provided as one or more taxonomy, tags, or semantic rules,that provide a continuum of targeting from rules with a more precisetargeting continuing through rules associated with a precise targeting,and wherein a determination of the content assets to be recommended thatbest match the dynamically-determined user profile or the requestattributes, is based at least on one or more of: a taxonomy of thecontent metadata associated with the content assets; or a semanticmatching of the content metadata associated with the content assets. 12.The method of claim 9, wherein the dynamically-determined user profileis based at least partly on data aggregated from a plurality of profilesources and describing a user profile and user profile attributesreceived from the profile sources via a service provider interface andconnector associated with each profile source.
 13. The method of claim9, wherein for each user of a plurality of users, the system tracks auser identity across a plurality of sessions or content channels, whichinformation is then used in generating the dynamically-determined userprofile.
 14. The method of claim 9, further comprising a recommendationbuilder, including a plurality of interface screens that: receive asinput, metadata associated with content assets; receive as input,definitions of types of user profiles; receive as input, definitions ofrecommendation rules; receive as input, definitions of a web orapplication content managed in the content management system, includeone or more blog posts, articles, images, videos, banners, documents,frequently accessed questions, data records, lists, success stories thatassociates particular user profiles and user profile attributes withparticular recommendations; and enable processing of different types ofuser profiles, recommendation rules, and success stories, against thecontent metadata associated with content assets, for displaying previewsof the delivery of personalized content across different channels. 15.The method of claim 9, wherein, the system enables search engineoptimization of a website by treating a search engine crawler as one ormore distinct profiles and serving distinct personalized content, whichenables the search engine crawler to find distinct content, fordetermining a search engine optimization score for the site.
 16. Themethod of claim 9, wherein comparison of user profile attributes withthe content metadata associated with the content assets, anddetermination of one or more content assets to return in response to arequest for personalized content is based on: a determination of contentassets that exactly match a user profile, or a determination of contentattributes that best match the user profile attributes; and determininga ranked collection of one or more content assets to return in responseto a request for personalized content.
 17. A non-transitory computerreadable storage medium, including instructions stored thereon whichwhen read and executed by one or more computers cause the one or morecomputers to perform a method comprising: providing, at a contentmanagement system, access to a repository of content assets; andprocessing requests, from client devices, for delivery of personalizedcontent, including determining a content channel and a user identityassociated with a request for personalized content, and determining,based on a content metadata associated with the content assets, thecontent channel associated with the request, and adynamically-determined user profile, a particular content asset todeliver in response to the request, wherein the dynamically-determineduser profile is based at least partly on user profile information andreal-time tracking of events received from multiple sources; retrievingthe particular content asset determined for delivery from the repositoryof content assets; and delivering the particular content asset to theclient device during via the content channel associated with therequest.